BAFF Expression Correlates with Idiopathic Inflammatory Myopathy Disease Activity Measures and Autoantibodies
Bibliographic record
Abstract
OBJECTIVE: To investigate B cell survival cytokine messenger RNA (mRNA) levels as biomarkers of idiopathic inflammatory myopathies (IIM). METHODS: We measured and compared mRNA levels of B cell survival cytokines by quantitative real-time polymerase chain reaction in 98 patients with IIM, 38 patients with systemic lupus erythematosus, and 21 healthy controls. The cytokines were B cell-activating factor belonging to the tumor necrosis factor family (BAFF); ΔBAFF; and a proliferation-inducing ligand (APRIL); and their receptors BAFF-R, transmembrane activator and calcium modulator and cyclophilin ligand interactor, and B cell maturation antigen (BCMA). We also identified autoantibodies, including anti-Sm, anti-RNP, anti-SSA/Ro, anti-SSB/La, anti-topoisomerase 1, anti-hystidyl-tRNA synthetase, anti-ribosomal P, and anti-chromatin. Clinical disease activity was assessed by the International Myositis Assessment and Clinical Studies core set tool. We examined correlation of mRNA with disease activity, medication use, and autoantibodies. RESULTS: We found a positive correlation of BAFF and ΔBAFF expression with 3 disease activity measures, with ΔBAFF having a stronger correlation. Similarly, anti-SSA/Ro-52 and/or anti-SSA/Ro-60 had a strong positive correlation with mRNA levels of BAFF and ΔBAFF, and with relative ratios of BAFF/APRIL and BCMA/BAFF-R. CONCLUSION: These findings highlight the potential importance of BAFF, ΔBAFF, and BAFF-R in the pathogenesis of IIM, and suggest an important role in the assessment of disease activity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".